A ML-Based High-Accuracy Estimation of Sampling and Carrier Frequency Offsets for OFDM Systems

Abstract

This paper addresses the problem of acquiring the sampling frequency offset (SFO) and carrier frequency offset (CFO), which severely degrade the performance of orthogonal frequency division multiplexing (OFDM) system. Using two identical frequency domain (FD) long training symbols in preamble, we propose a novel maximum-likelihood (ML) estimation method to simultaneously acquire the values of SFO and CFO, which extend the Kim’s and Wang’s estimation methods. The main contribution of this paper is that the first-order Legendre series expansion is used to obtain the SFO and CFO values in closed-form. For obtaining the performance of the proposed estimation scheme, we built the OFDM system model according to IEEE 802.11a. The results show that the proposed scheme achieves the best performance to the existing schemes.